Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images
Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a targe...
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| Published in: | Remote sensing (Basel, Switzerland) Vol. 17; no. 15; p. 2581 |
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| Main Authors: | , , , , |
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| Language: | English |
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| ISSN: | 2072-4292, 2072-4292 |
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| Abstract | Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets. |
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| AbstractList | Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. These inherent heterogeneities present substantial challenges for change detection, a task that involves identifying changes in a target area by analyzing multi-temporal images. To address this issue, we propose the Multi-Head Graph Attention Mechanism (MHGAN), designed to achieve accurate detection of surface changes in heterogeneous remote sensing images. The MHGAN employs a bidirectional adversarial convolutional autoencoder network to reconstruct and perform style transformation of heterogeneous images. Unlike existing unidirectional translation frameworks (e.g., CycleGAN), our approach simultaneously aligns features in both domains through multi-head graph attention and dynamic kernel width estimation, effectively reducing false changes caused by sensor heterogeneity. The network training is constrained by four loss functions: reconstruction loss, code correlation loss, graph attention loss, and adversarial loss, which together guide the alignment of heterogeneous images into a unified data domain. The code correlation loss enforces consistency in feature representations at the encoding layer, while a density-based kernel width estimation method enhances the capture of both local and global changes. The graph attention loss models the relationships between features and images, improving the representation of consistent regions across bitemporal images. Additionally, adversarial loss promotes style consistency within the shared domain. Our bidirectional adversarial convolutional autoencoder simultaneously aligns features across both domains. This bilateral structure mitigates the information loss associated with one-way mappings, enabling more accurate style transformation and reducing false change detections caused by sensor heterogeneity, which represents a key advantage over existing unidirectional methods. Compared with state-of-the-art methods for heterogeneous change detection, the MHGAN demonstrates superior performance in both qualitative and quantitative evaluations across four benchmark heterogeneous remote sensing datasets. |
| Author | Jia, Meng Lou, Xiangyu Shi, Zhenghao Lu, Xiaofeng Zhao, Zhiqiang |
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| Cites_doi | 10.1109/TGRS.2017.2739800 10.1080/01431161.2018.1448481 10.1109/TIP.2021.3093766 10.1109/IGARSS.2010.5650677 10.1007/978-3-030-30671-7 10.1109/TIP.2017.2784560 10.3390/s8031613 10.1109/JPROC.2015.2462751 10.3390/rs11202377 10.1109/TGRS.2009.2038274 10.1109/TNNLS.2016.2636227 10.1109/LGRS.2018.2868704 10.1016/j.patcog.2020.107598 10.1177/001316448104100307 10.1109/LGRS.2022.3201925 10.3390/rs11091091 10.1109/TNNLS.2022.3172183 10.1016/j.rse.2009.09.012 10.1109/TGRS.2005.857987 10.1109/TGRS.2011.2171493 10.1080/01431168908903939 10.1109/MGRS.2018.2890023 10.1109/LGRS.2019.2892432 10.1109/JSTARS.2019.2916560 10.1109/IGARSS.2015.7326153 10.1109/CVPR.2017.632 10.3390/rs15030621 10.1007/978-3-642-35289-8_3 10.1109/TGRS.2007.893568 10.1109/TIP.2004.838698 |
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| SubjectTerms | Change detection Classification Data structures Datasets Deep learning domain transformation graph attention Heterogeneity heterogeneous remote sensing images Image acquisition Methods Neural networks Performance evaluation Remote sensing Representations Sensors Similarity measures Transformations (mathematics) unsupervised change detection |
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